12 Data Science Hackathons to Test Your Skills

If you are passionate about technology, primarily coding and you have a desire to tackle business problems and innovate, you need a platform or environment that helps you with ideation, problem solving, collaborative learning, transformation in thinking. Hackathons are one of the best ways to go about it. Hackathon is a social coding event where computer programmers and other enthusiasts come together and solve interesting and real-world business problems by building a software program. Hackathon is a blend of words – hacker (skilled computer programmer) and marathon. Alternative names to hackathons include codefest, hack day etc.

Hackathons help you to put your coding skills to work and improve them. Through hackathons essentially one can learn new data science skills, build things and get hired as well. In addition to having a competitive element, hackathons provide an excellent platform to meet and collaborate with new people and also connect with experts in your industry. Along with all the learning, as a bonus mostly hackathons come with prize money or/and consideration for hiring.

Hackathons can happen virtually as well. Hackathon events are generally meant for a short period of time such as 16 to 24 hours, hence the participants are supposed to work rapidly and often work without sleep to achieve their task. But nowadays, we see good number of hackathons that run for a week to month as well (in the form of ‘coding contest or a hiring challenge’) 

What Is Data Science Hackathon?

Data science is one of the sought after fields due to its footprints in a wide array of applications including agriculture, healthcare, manufacturing, banking, advertising, retail, marketing etc. Visit here to attend the best online data science bootcamps . 

A data science hackathon is an event or hackathon where data scientists, machine learning engineers, MLOps engineers, data engineers and other data science enthusiasts participate, collaborate with one another and work on a data science business problem. Business problems can involve processing of simple structured data (CRM, Sales, Excel etc.) or/and unstructured data (emails, voice transcriptions, social media, ratings & reviews etc.) through various techniques of NLP, Computer Vision and Speech Processing. 

Top 12 Data Science Hackathons

1. Hackerearth

Hackerearth provides enterprise software that helps organizations with their technical hiring needs. HackerEarth is used by organizations for technical skill assessment and remote video interviewing. Company is helping software developers be better through coding contests, data science competitions, and hackathons. It offers hackathons for both developers and businesses. Company provides businesses a platform to host virtual hackathons to engage internal and external talent. 

Sirion-hackfest-binary-utopia is one of the recent hackathons on this platform. The event was about evaluating supplier performance based on multiple attributes, as it would not only help clients mitigate risk but also help reduce costs. 1st prize was INR 200K, 2nd prize was 125K and 75K was 3rd prize. Additionally, AirPods vouchers and goodies were up for grabs. 

2. Machinehack 

MachineHack is an online platform for Machine Learning competitions, assessment and hiring. You can grow your data science skills by competing in industry-curated hackathons that include machine learning/deep learning hackathons, visualization and data engineering challenges.

One of the recently concluded hackathons for data science on machinehack was Predict The News Category Hackathon. In this hackathon, data science and machine learning enthusiasts were expected to use NLP(Natural Language Processing) to predict which genre or category a piece of news will fall into from the story. There were four distinct sections into which each story may fall. The Sections are labeled as follows : Politics: 0 Technology: 1 Entertainment: 2 Business: 3 

3. Kaggle

If you are a data scientist, it is highly unlikely that you have not heard of kaggle. Kaggle is an online community of data scientists and machine learning practitioners. It offers a platform to collaborate with other users, find and publish datasets and participate in data science challenges to compete with other data scientists. Attend this data science certification course to master data science.

You can find all the active hackathons on Kaggle here. One of the ongoing hackathons on Kaggle is American Express – Default Prediction. In this competition, you’ll apply your machine learning skills to predict credit default. Specifically, you will leverage an industrial scale data set to build a machine learning model that challenges the current model in production. Training, validation, and testing datasets include time-series behavioral data and anonymized customer profile information. Top solutions could challenge the credit default prediction model used by the world’s largest payment card issuer earning you cash prizes, the opportunity to interview with American Express, and potentially a rewarding new career. 


International Data Analysis Olympiad(IDAO) is a contest organized by HSE University and Yandex. The event aims to bridge the gap between the all-increasing complexity of Machine Learning models and performance bottlenecks of the industry. The participants will strive not only to maximize the quality of their predictions but also to devise resource-efficient algorithms. To kickstart your career in data science, attend KnowledgeHut best online data science bootcamps. 

5th edition of IDAO concluded in April, 2022. In the qualification round having a labeled training data set, participants were asked to make a prediction for the test data and submit their predictions to the leaderboard. In this track, participants could produce arbitrarily complex models. In the final round, participants had 36 hours to solve a task from Otkritie Bank. 

5. Datahack

Datahack is a hackathon platform by Analytics Vidhya. It hosts data science Competitions to compete, win, practice, learn and build your Data Science portfolio. Data science hackathons on DataHack enable you to compete with leading data scientists and machine learning experts in the world. It provides an opportunity to work on real life data science problems, improve your skill set, learn from expert data science and machine learning professionals, and hack your way to the top of the hackathon leaderboard! You also stand a chance to win prizes and get a job at your dream data science company. 

One of the recently concluded hackathons on this platform was Food demand forecasting. Without proper demand forecasting processes in place, it can be nearly impossible to have the right amount of stock on hand at any given time. Too much inventory in the warehouse means more risk of wastage, and not enough could lead to out-of-stocks and push customers to seek solutions from your competitors. In this challenge, get a taste of the demand forecasting challenge using a real dataset. 

6. Dphi

Dphi is an open education AI community. As part of community initiatives, they frequently conduct datathons, bootcamps on cutting-edge technologies such as Deep Learning, Machine Learning, etc, and host live sessions with industry experts. Hackathons at Dphi are AI Challenges that simulate real-world problems. It is a great place to put your AI/Data Science skills to test on diverse datasets allowing you to foster learning through competitions. 

The recently concluded hackathon on Dphi was on Predict Career Longevity for NBA Rookies. Career longevity is dependent on various factors for any players in all the games and so for NBA Rookies. The factors like games played against a given opponent, count of total games played, and other statistics of the player during the game were judged. Objective was to use machine learning techniques to determine if a player’s career will flourish or not. 

7. AICrowd 

AIcrowd enables data science, experts and enthusiasts, to collaboratively solve real-world problems, through challenges. AIcrowd hosts hackathons that tackle diverse problems in Artificial Intelligence with real-world impact. AIcrowd Community spearheads the state of the art, be it advanced RL innovation or applications of ML in scientific research. There is an interesting problem for everyone. 

One of the recently concluded hackathons on AICrowd was Food Recognition Benchmark 2022. The goal of this benchmark was to train models which can look at images of food items and detect the individual food items present in them. They use a novel dataset of food images collected through the MyFoodRepo app, where numerous volunteer Swiss users provide images of their daily food intake in the context of a digital cohort called Food & You. 

8. Techgig 

TechGig is a division of Times Internet Limited, India’s largest digital products company. They are India’s largest and fastest growing developer community of 4.2 million software professionals. They are an innovative and enthusiastic technology community of super-active developers who love to compete and showcase their skills, learn new technologies, and keep up with the latest technology news to grow in their career. 

Participating in their hackathons will improve your knowledge on niche skills like IoT, Machine Learning, Mobility, User Experience, etc. These smart challenges have been designed by experts to test how familiar you are with various technologies and platforms. Participating in these hackathons will improve your confidence levels in the programming language concerned and make you think on your feet, a trait that can help you land up better job opportunities in the future. 

One of the recently concluded hackathons was “The SBI – Innovate for Bank 2022”, which was an online challenge, co-powered by Microsoft Azure, for tech enthusiasts and software developers to suggest tech-based solutions for business challenges faced by the SBI. One of the problem statements was Voice biometrics. Goal was to develop a voice signature for a sample voice clip, for authentication with other voice samples and further develop the voice signature as per repeated voice samples. 


DrivenData brings cutting-edge practices in data science and crowdsourcing to some of the world’s biggest social challenges and the organizations taking them on. They host online challenges, usually lasting 2-3 months, where a global community of data scientists competes to come up with the best statistical model for difficult predictive problems that make a difference. DrivenData works on projects at the intersection of data science and social impact, in areas like international development, health, education, research and conservation, and public services. 

One of the active hackathons on this platform is Richter’s Predictor: Modeling Earthquake Damage. Based on aspects of building location and construction, your goal is to predict the level of damage to buildings caused by the 2015 Gorkha earthquake in Nepal. The data was collected through surveys by Kathmandu Living Labs and the Central Bureau of Statistics, which works under the National Planning Commission Secretariat of Nepal. 

10. Zindi 

Zindi hosts the largest community of African data scientists, working to solve the world’s most pressing challenges using machine learning and AI. It connects data scientists with organizations, and provides a place to learn, hone your skills and find a job. They want to transform the African continent and showcase African data science talent to the world. 

You can find the active hackathons on the platform. Upcoming hackathon is – DataFest Africa Noise Pollution Classification Challenge. Due to the widespread noise pollution in African cities, data sponsors Sunbird AI recognized a need to gather data on noise exposure, in order to produce an action plan and empower citizens to be vigilant in tracking and monitoring noise. The challenge is to train a noise classification model to classify noise into different categories. 

11. Topcoder

Topcoder is a crowdsourcing company with an open global community of designers, developers, data scientists, and competitive programmers. Topcoder pays community members for their work on the projects and sells community services to corporate, mid-size, and small-business clients. 

You can find active hackathons on the platform. One of the ongoing interesting hackathons is NASA Comet Detection Marathon. The purpose of this project is to develop an Artificial Intelligence (AI)/Machine Learning (ML) tool that will help astronomers detect very faint comets that approach the sun, referred to as “sungrazing comets”. Scientists at NASA want to improve their ability to detect very dim “category C” comets. 

12. Datacamp

Datacamp offers up-skilling at scale. It provides data training designed for the businesses. It newly launched hackathons on their platform. They help you grow your data science skills by solving real-world problems. They are designed for beginners and advanced learners alike, their data science competitions help test your knowledge and get hands-on data science experience. This is one of the data science hackathon for beginners. 

One of their upcoming hackathons is Can you find a better way to segment your customers?. Business problem is that one of the medical device manufacturers sells orthopedic devices directly to individual doctors who use them on rehabilitation and physical therapy patients. Historically, the sales and customer support departments have grouped doctors by geography. However, the region is not a good predictor of the number of purchases a doctor will make or their support needs. Goal is to use a data-centric approach to segmenting doctors to improve marketing 


As we have seen with all the above discussed top data science hackathons, whether you have just kickstarted your career in data science or you’re an experienced and seasoned data scientist, you will always have something to take away from data science hackathons.

By participating in data science hackathons, you get to sneak peek into real-world, next-gen, unsolved business problems. You get to learn ideation, access to interesting datasets, state-of-the-art methods/algorithms/approaches/techniques/models, recent trends in data science, rapid coding, optimization methods, work on diverse data science topics – NLP, Computer Vision, Speech, Recommendation systems, Chatbots etc. Other exciting rewards of hackathons include prize money, vouchers, goodies, opportunity for interviewing/hiring, and it can actually get you to land in your dream job as well. Most important takeaway from a hackathon can be the whole process of collaborative learning and knowledge sharing. 

Frequently Asked Questions(FAQs)

1. How do you approach a data science Hackathon?

To start with you should team up with someone interested and enthusiastic about data science, so that it provides a space for collaborative learning. It’s essential to have exposure to both research and industrial aspects of data science, so that it ensures you’re updated with state-of-the-art techniques, and you know how to write optimal code for a given business problem. One should be strong in EDA(Exploratory Data Analysis), feature engineering and applying ensemble methods, to be able to crack a data science hackathon. 

2. Is a data scientist a hacker?

Hacker is a person with computer security knowledge and uses programming skills and computer technology to overcome a challenge or a problem. Data scientist mainly requires math and statistics skills. One can use data science to analyze and prevent cybersecurity woes. Data science and hacking can go hand in hand to make or break the systems. Data scientists have to study cybersecurity tools to find cybersecurity threats, and through this, they can predict risk on experience and behavior patterns. 

3. Which coding is best for data science?

Python is the best coding language for data science as it is the most popular and widely used programming language for data science/machine learning. Python is equipped with powerful libraries and frameworks such as PyTorch, Tensorflow, PyCaret, NumPy, SciPy and pandas etc.

4. Is Python sufficient for data science?

While Python is most popular and widely used in this field, with a very strong and active community, it may not be sufficient knowing python alone depending upon your objective. If you want to make a mark in the data science research field, it’s highly advisable to know R, as it comes with specialized libraries for statistical analysis and intuitive visualizations. If your goal is to build a pipeline for big data involving manipulation of structured data, always consider SQL. If you are closely working with core technology of parallel and distributed computing, it’s beneficial to know Julia. 

5. Is C++ required for data science?

If the goal is to build highly functional data science based tools and have the ability to compile the data quickly, or/and you want to cross-compile the code for edge devices/embedded targets, knowing C++ makes you a powerful embedded data scientist. 

6. What is the most popular language for data science?

As per the several programming language popularity indices, including that of TIOBE, python is easily the most popular language for data science by a significant margin. Python is a high-level, interpreted, general-purpose programming language. 

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